{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Collaborative Filtering using Matrix Factorization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![CF](imgs/netflixCF.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Intro\n",
    "The content based engine suffers from some severe limitations. It is only capable of suggesting movies which are close to a certain movie. That is, it is not capable of capturing tastes and providing recommendations across genres.\n",
    "\n",
    "Also, the engine that we built is not really personal in that it doesn't capture the personal tastes and biases of a user. Anyone querying our engine for recommendations based on a movie will receive the same recommendations for that movie, regardless of who she/he is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.sparse.linalg import svds\n",
    "from wordcloud import WordCloud\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "moviesDF=pd.read_csv(\"ml-1m/movies.dat\",sep='::',engine=\"python\",header=None)\n",
    "moviesDF.columns=[\"movieId\",\"title\",\"genres\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preprocessing\n",
    "\n",
    "drop unused columns and set internal movies and users unique sequential identifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def prepareDataset(myMovies):\n",
    "    print(\"======= Preparing Dataset =======\")\n",
    "    ratings = pd.read_csv('ml-1m/ratings.dat',sep='::',engine=\"python\",header=None)\n",
    "    ratings.columns=[\"userID\",\"movieID\",\"rating\",\"timestamp\"]\n",
    "    ratings.drop(columns=[\"timestamp\"],inplace=True)\n",
    "    for movie in myMovies:\n",
    "        if movie['id'] in moviesDF.movieId.values[:]:\n",
    "            ratings=ratings.append({\"userID\":0,\"movieID\":movie['id'],\"rating\":float(movie['rating'])},ignore_index=True)\n",
    "    ratings[\"movie_id\"] = ratings.movieID.astype('category').cat.codes.values\n",
    "    ratings[\"user_id\"] = ratings.userID.astype('category').cat.codes.values\n",
    "    return ratings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Building  ( ** Matrix Factorization ** )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The idea behind such models is that attitudes or preferences of a user can be determined by a small number of hidden latent factors. These factors are also called Embeddings, which represent different characteristics for users and items. \n",
    "\n",
    "Matrix factorization can be done by various methodsincluding:\n",
    "- ** Singular Values Decomposition (SVD) **\n",
    "- Probabilistic Matrix Factorization (PMF) \n",
    "- Non-Negative Matrix Factorization (NMF)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def factorizeMatrix(Ratings):\n",
    "    #Ratings = ratings.pivot(index = 'user_id', columns ='movieID', values = 'rating').fillna(0)\n",
    "    R = Ratings.as_matrix()\n",
    "    user_ratings_mean = np.mean(R, axis = 1)\n",
    "    Ratings_demeaned = R - user_ratings_mean.reshape(-1, 1)\n",
    "    U, sigma, Vt = svds(Ratings_demeaned, k = 50)\n",
    "    sigma = np.diag(sigma)\n",
    "    uSigmaProd=np.dot(U, sigma)\n",
    "    return uSigmaProd,Vt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![svd](imgs/svd.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The SVD allows to describe the effect of a matrix A on a vector (via the matrix-vector product), as a three-step process A=UΣV†:\n",
    "\n",
    "    - A first rotation in the input space (V) (m x n)\n",
    "    - A simple positive scaling that takes a vector in the input space to the output space (Σ) (n x m)\n",
    "    - And another rotation in the output space (U) (m x n)\n",
    "\n",
    "    - Note that V† denotes the conjugate of V⊤, hence the two are equal when V is real.\n",
    "\n",
    "- The singular value decomposition and the eigendecomposition are closely related. Namely:\n",
    "\n",
    "    - The left-singular vectors of M are eigenvectors of MM*.\n",
    "    - The right-singular vectors of M are eigenvectors of M*M.\n",
    "    - The non-zero singular values of M (found on the diagonal entries of Σ) are the square roots of the non-zero eigenvalues of both M*M and MM*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dotProd(uSigmaProd,Vt,Ratings):\n",
    "    R = Ratings.as_matrix()\n",
    "    user_ratings_mean = np.mean(R, axis = 1)\n",
    "    vtProd=np.dot(uSigmaProd, Vt)\n",
    "    all_user_predicted_ratings = vtProd + user_ratings_mean.reshape(-1, 1)\n",
    "    preds = pd.DataFrame(all_user_predicted_ratings, columns = Ratings.columns)\n",
    "    return preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def buildModel(ratings):\n",
    "    Ratings = ratings.pivot(index = 'user_id', columns ='movieID', values = 'rating').fillna(0)\n",
    "    uSigmaProd,Vt = factorizeMatrix(Ratings)\n",
    "    return dotProd(uSigmaProd,Vt,Ratings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparing Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def recommendMovies(ratedMovies=[],num_recommendations=10):\n",
    "    r=pd.DataFrame()\n",
    "    for i in range(len(ratedMovies)):\n",
    "        try:\n",
    "            r=r.append(moviesDF[moviesDF.movieId==ratedMovies[i]['id']],ignore_index=True)\n",
    "        except:\n",
    "            continue\n",
    "            \n",
    "    #----- wordcloud of user movies -----\n",
    "    wordcloud = WordCloud().generate(str(r.genres.values).replace(\"'\",''))\n",
    "    plt.imshow(wordcloud, interpolation='bilinear')\n",
    "    plt.axis(\"off\")\n",
    "    plt.title(\"watched movies genres\")\n",
    "    plt.show()\n",
    "    #------------------------------------\n",
    "    \n",
    "    original_ratings=prepareDataset(ratedMovies)\n",
    "    userID = 0\n",
    "    movies=moviesDF[['movieId', 'title','genres']]\n",
    "    preds=buildModel(original_ratings)\n",
    "    sorted_user_predictions = preds.iloc[userID].sort_values(ascending=False)\n",
    "    user_data = original_ratings[original_ratings.user_id == (userID)]\n",
    "\n",
    "    user_full = (user_data.merge(movies, how = 'left', left_on = 'movieID', right_on = 'movieId').\n",
    "                     sort_values(['rating'], ascending=False))\n",
    "    print(\"======= Finshing Recommendations =======\")\n",
    "    unratedMovies=((movies[~movies['movieId'].isin(user_full['movieID'])]))\n",
    "    \n",
    "    recommendations = unratedMovies.merge(pd.DataFrame(sorted_user_predictions).reset_index(), how = 'left',\n",
    "               left_on = 'movieId',right_on = 'movieID').rename(\n",
    "        columns = {userID: 'Predictions'}).sort_values('Predictions', ascending = False).iloc[:num_recommendations, :-1]\n",
    "    recommendations.drop(columns=['movieID'],inplace=True)\n",
    "    \n",
    "    #----- wordcloud of user recommendations -----\n",
    "    wordcloud = WordCloud().generate(str(recommendations.genres.values).replace(\"'\",\"\"))\n",
    "    plt.imshow(wordcloud, interpolation='bilinear')\n",
    "    plt.axis(\"off\")\n",
    "    plt.title(\"recommended movies genres\")\n",
    "    plt.show()\n",
    "    #------------------------------------\n",
    "    \n",
    "    \n",
    "    return recommendations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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HmTpX4N2/dg/bHuzga//6NQafn+DZf/UAHXuTRBI6Tt3joZ/fT6LT4q8/8RrD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\nKSkpKWeY/x+zRRigKsn6VQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "======= Preparing Dataset =======\n",
      "======= Finshing Recommendations =======\n"
     ]
    },
    {
     "data": {
      "image/png": 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+8rubeO5zAxz9wdjqZTyXEruY96LtEg1ogRB2IQt4/gAzVkchOYyiqgiu9bT1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\nfPmqOw1VaESDjehaiPaGvWiKDkgUoWHoEerjG5HS5dzwq6xt2kdrze0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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>50</td>\n",
       "      <td>Usual Suspects, The (1995)</td>\n",
       "      <td>Crime|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1192</th>\n",
       "      <td>1213</td>\n",
       "      <td>GoodFellas (1990)</td>\n",
       "      <td>Crime|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1070</th>\n",
       "      <td>1089</td>\n",
       "      <td>Reservoir Dogs (1992)</td>\n",
       "      <td>Crime|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601</th>\n",
       "      <td>608</td>\n",
       "      <td>Fargo (1996)</td>\n",
       "      <td>Crime|Drama|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>593</td>\n",
       "      <td>Silence of the Lambs, The (1991)</td>\n",
       "      <td>Drama|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1572</th>\n",
       "      <td>1617</td>\n",
       "      <td>L.A. Confidential (1997)</td>\n",
       "      <td>Crime|Film-Noir|Mystery|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1676</th>\n",
       "      <td>1729</td>\n",
       "      <td>Jackie Brown (1997)</td>\n",
       "      <td>Crime|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3041</th>\n",
       "      <td>3114</td>\n",
       "      <td>Toy Story 2 (1999)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2195</th>\n",
       "      <td>2268</td>\n",
       "      <td>Few Good Men, A (1992)</td>\n",
       "      <td>Crime|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>293</td>\n",
       "      <td>Professional, The (a.k.a. Leon: The Profession...</td>\n",
       "      <td>Crime|Drama|Romance|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>356</td>\n",
       "      <td>Forrest Gump (1994)</td>\n",
       "      <td>Comedy|Romance|War</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2689</th>\n",
       "      <td>2762</td>\n",
       "      <td>Sixth Sense, The (1999)</td>\n",
       "      <td>Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>318</td>\n",
       "      <td>Shawshank Redemption, The (1994)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2318</th>\n",
       "      <td>2391</td>\n",
       "      <td>Simple Plan, A (1998)</td>\n",
       "      <td>Crime|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1653</th>\n",
       "      <td>1704</td>\n",
       "      <td>Good Will Hunting (1997)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2507</th>\n",
       "      <td>2580</td>\n",
       "      <td>Go (1999)</td>\n",
       "      <td>Crime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1436</th>\n",
       "      <td>1466</td>\n",
       "      <td>Donnie Brasco (1997)</td>\n",
       "      <td>Crime|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>Heat (1995)</td>\n",
       "      <td>Action|Crime|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>778</td>\n",
       "      <td>Trainspotting (1996)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1596</th>\n",
       "      <td>1645</td>\n",
       "      <td>Devil's Advocate, The (1997)</td>\n",
       "      <td>Crime|Horror|Mystery|Thriller</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     movieId                                              title  \\\n",
       "47        50                         Usual Suspects, The (1995)   \n",
       "1192    1213                                  GoodFellas (1990)   \n",
       "1070    1089                              Reservoir Dogs (1992)   \n",
       "601      608                                       Fargo (1996)   \n",
       "586      593                   Silence of the Lambs, The (1991)   \n",
       "1572    1617                           L.A. Confidential (1997)   \n",
       "1676    1729                                Jackie Brown (1997)   \n",
       "3041    3114                                 Toy Story 2 (1999)   \n",
       "2195    2268                             Few Good Men, A (1992)   \n",
       "288      293  Professional, The (a.k.a. Leon: The Profession...   \n",
       "349      356                                Forrest Gump (1994)   \n",
       "2689    2762                            Sixth Sense, The (1999)   \n",
       "312      318                   Shawshank Redemption, The (1994)   \n",
       "2318    2391                              Simple Plan, A (1998)   \n",
       "1653    1704                           Good Will Hunting (1997)   \n",
       "2507    2580                                          Go (1999)   \n",
       "1436    1466                               Donnie Brasco (1997)   \n",
       "4          6                                        Heat (1995)   \n",
       "765      778                               Trainspotting (1996)   \n",
       "1596    1645                       Devil's Advocate, The (1997)   \n",
       "\n",
       "                                genres  \n",
       "47                      Crime|Thriller  \n",
       "1192                       Crime|Drama  \n",
       "1070                    Crime|Thriller  \n",
       "601               Crime|Drama|Thriller  \n",
       "586                     Drama|Thriller  \n",
       "1572  Crime|Film-Noir|Mystery|Thriller  \n",
       "1676                       Crime|Drama  \n",
       "3041       Animation|Children's|Comedy  \n",
       "2195                       Crime|Drama  \n",
       "288       Crime|Drama|Romance|Thriller  \n",
       "349                 Comedy|Romance|War  \n",
       "2689                          Thriller  \n",
       "312                              Drama  \n",
       "2318                    Crime|Thriller  \n",
       "1653                             Drama  \n",
       "2507                             Crime  \n",
       "1436                       Crime|Drama  \n",
       "4                Action|Crime|Thriller  \n",
       "765                              Drama  \n",
       "1596     Crime|Horror|Mystery|Thriller  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Titanic - Seven - pulb fiction - toy story\n",
    "recommendMovies([{'id':1721,'rating':1},{'id':47,'rating':3},{'id':296,'rating':3},{'id':1,'rating':1}],20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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